The relationship between physical activity and post-operative length of hospital stay: A systematic review

Abeles, Aliza; Kwasnicki, Richard M.; Pettengell, Christopher; Murphy, Jamie; Darzi, Ara

doi:10.1016/j.ijsu.2017.06.085

Cited by 39 publications

(35 citation statements)

References 36 publications

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“…Wearable sensor-based measurements [15] may provide valid and reliable data about physical activity in different hospital discharged populations such as people who have had a stroke [16], joint replacement [14], or cardiac surgery [11]. Systematic reviews have concluded that objective physical activity data collected by body-worn sensors may be capable of predicting functional recovery post-operatively [17].…”

Section: Introductionmentioning

confidence: 99%

Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

et al. 2019

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Background. Mobility impairment is common in older adults and negatively influences the quality of life. Mobility level may change rapidly following surgery or hospitalization in the elderly. The Timed Up and Go (TUG) is a simple, frequently used clinical test for functional mobility; however, TUG requires supervision from a trained clinician, resulting in infrequent assessments. Additionally, assessment by TUG in clinic settings may not be completely representative of the individual's mobility in their home environment. Objective. In this paper, we introduce a method to estimate TUG from activities detected in free-living, enabling continuous remote mobility monitoring without expert supervision. The method is used to monitor changes in mobility following Total Hip Arthroplasty (THA). Methods. Community-living elderly (n=239, 65-91 years) performed a standardized TUG in a laboratory and wore a wearable pendant device that recorded accelerometer and barometric sensor data for at least 3-days. Activities of daily living, including walks and sit-to-stand transitions, and their related mobility features were extracted and used to develop a regularized linear model for remote TUG test estimation. Changes in the remote TUG were evaluated in orthopaedic patients (n=15, 55-75 years), during 12-weeks following THA. Results. In leave-one-out-cross-validation, a strong correlation (ρ=0.70) was observed between the new remote TUG and standardized TUG times. Test-retest reliability of 3-day estimates was high (ICC=0.94). Compared to 2-weeks post-THA, remote TUG was significantly improved at 6-weeks (11.7±3.9 s vs 8.0±1.8 s, p<0.001), with no further change at 12-weeks (8.1±3.9 s, p=0.37). Conclusions. Remote TUG can be estimated in older adults using 3-days of ADLs data recorded using a wearable pendant. Remote TUG has discriminatory potential for identifying frail elderly and may provide a convenient way to monitor changes in mobility in unsupervised settings.

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Section: Introductionmentioning

confidence: 99%

Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

et al. 2019

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“…Low amounts of PA and prolonged periods of uninterrupted sedentary activity during hospitalization have been associated with functional decline [29,30], a decline in physical performance [30], increased insulin resistance [30], increased length of stay [31] , increased risk of institutionalization [16], and mortality [29,[32][33][34]. To reduce the risks of these…”

Section: Introductionmentioning

confidence: 99%

Optimization and Validation of a Classification Algorithm for Assessment of Physical Activity in Hospitalized Patients

Dijk-Huisman

Bijnens

Senden

et al. 2021

Sensors

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Low amounts of physical activity (PA) and prolonged periods of sedentary activity are common in hospitalized patients. Objective PA monitoring is needed to prevent the negative effects of inactivity, but a suitable algorithm is lacking. The aim of this study is to optimize and validate a classification algorithm that discriminates between sedentary, standing, and dynamic activities, and records postural transitions in hospitalized patients under free-living conditions. Optimization and validation in comparison to video analysis were performed in orthopedic and acutely hospitalized elderly patients with an accelerometer worn on the upper leg. Data segmentation window size (WS), amount of PA threshold (PA Th) and sensor orientation threshold (SO Th) were optimized in 25 patients, validation was performed in another 25. Sensitivity, specificity, accuracy, and (absolute) percentage error were used to assess the algorithm’s performance. Optimization resulted in the best performance with parameter settings: WS 4 s, PA Th 4.3 counts per second, SO Th 0.8 g. Validation showed that all activities were classified within acceptable limits (>80% sensitivity, specificity and accuracy, ±10% error), except for the classification of standing activity. As patients need to increase their PA and interrupt sedentary behavior, the algorithm is suitable for classifying PA in hospitalized patients.

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“…For example, step count monitors are common and physical activity has been tied to favorable postoperative outcomes [7,8]. However, it is difficult to know exactly how step count modifies variables other than length of stay [9,10] or if length of stay can serve as a surrogate for impending complications [11]. As investigators seek to incorporate mobile sensors into ERAS pathways, there is a need for a synthesized evaluation of how mobile sensor output data correlate to postoperative outcomes.…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Mobile Digital Sensor Monitoring and Perioperative Outcomes: Systematic Review

et al. 2021

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Background Monitoring surgical recovery has traditionally been confined to metrics measurable within the hospital and clinic setting. However, commercially available mobile sensors are now capable of extending measurements into a patient’s home. As these sensors were developed for nonmedical applications, their clinical role has yet to be established. The aim of this systematic review is to evaluate the relationship between data generated by mobile sensors and postoperative outcomes. Objective The objective of this study is to describe the current use of mobile sensors in the perioperative setting and the correlation between their data and clinical outcomes. Methods A systematic search of EMBASE, MEDLINE, and Cochrane Library from inception until April 2019 was performed to identify studies of surgical patients monitored with mobile sensors. Sensors were considered if they collected patient metrics such as step count, temperature, or heart rate. Studies were included if patients underwent major surgery (≥1 inpatient postoperative day), patients were monitored using mobile sensors in the perioperative period, and the study reported postoperative outcomes (ie, complications and hospital readmission). For studies including step count, a pooled analysis of the step count per postoperative day was calculated for the complication and noncomplication cohorts using mean and a random-effects linear model. The Grading of Recommendations, Assessment, Development, and Evaluation tool was used to assess study quality. Results From 2209 abstracts, we identified 11 studies for review. Reviewed studies consisted of either prospective observational cohorts (n=10) or randomized controlled trials (n=1). Activity monitors were the most widely used sensors (n=10), with an additional study measuring temperature, respiratory rate, and heart rate (n=1). Low step count was associated with worse postoperative outcomes. A median step count of around 1000 steps per postoperative day was associated with adverse surgical outcomes. Within the studies, there was heterogeneity between the type of surgery and type of reported postoperative outcome. Conclusions Despite significant heterogeneity in the type of surgery and sensors, low step count was associated with worse postoperative outcomes across surgical specialties. Further studies and standardization are needed to assess the role of mobile sensors in postoperative care, but a threshold of approximately 1000 steps per postoperative day warrants further investigation.

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The relationship between physical activity and post-operative length of hospital stay: A systematic review

Abstract: The amount of physical activity performed post-operatively negatively correlates with the length of hospital stay. This suggests that objective physical activity data collected by body worn sensors may be capable of predicting functional recovery post-operatively.

Cited by 39 publications

References 36 publications

Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

Optimization and Validation of a Classification Algorithm for Assessment of Physical Activity in Hospitalized Patients

Relationship Between Mobile Digital Sensor Monitoring and Perioperative Outcomes: Systematic Review

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